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Chen D, Chitre AS, Nguyen KMH, Cohen K, Peng B, Ziegler KS, Okamoto F, Lin B, Johnson BB, Sanches TM, Cheng R, Polesskaya O, Palmer AA. A Cost-effective, High-throughput, Highly Accurate Genotyping Method for Outbred Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603984. [PMID: 39071405 PMCID: PMC11275765 DOI: 10.1101/2024.07.17.603984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Affordable sequencing and genotyping methods are essential for large scale genome-wide association studies. While genotyping microarrays and reference panels for imputation are available for human subjects, non-human model systems often lack such options. Our lab previously demonstrated an efficient and cost-effective method to genotype heterogeneous stock rats using double-digest genotyping-by-sequencing. However, low-coverage whole-genome sequencing offers an alternative method that has several advantages. Here, we describe a cost-effective, high-throughput, high-accuracy genotyping method for N/NIH heterogeneous stock rats that can use a combination of sequencing data previously generated by double-digest genotyping-by-sequencing and more recently generated by low-coverage whole-genome-sequencing data. Using double-digest genotyping-by-sequencing data from 5,745 heterogeneous stock rats (mean 0.21x coverage) and low-coverage whole-genome-sequencing data from 8,760 heterogeneous stock rats (mean 0.27x coverage), we can impute 7.32 million bi-allelic single-nucleotide polymorphisms with a concordance rate >99.76% compared to high-coverage (mean 33.26x coverage) whole-genome sequencing data for a subset of the same individuals. Our results demonstrate the feasibility of using sequencing data from double-digest genotyping-by-sequencing or low-coverage whole-genome-sequencing for accurate genotyping, and demonstrate techniques that may also be useful for other genetic studies in non-human subjects.
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Affiliation(s)
- Denghui Chen
- Bioinformatics and System Biology Program, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Apurva S. Chitre
- Bioinformatics and System Biology Program, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Khai-Minh H. Nguyen
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Katarina Cohen
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Beverly Peng
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Kendra S. Ziegler
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Faith Okamoto
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Bonnie Lin
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Benjamin B. Johnson
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Thiago M. Sanches
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Riyan Cheng
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Abraham A. Palmer
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
- Institute for Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
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Borger MJ, Weissing FJ, Boon E. Human genomic data have different statistical properties than the data of randomised controlled trials. Behav Brain Sci 2023; 46:e184. [PMID: 37694897 DOI: 10.1017/s0140525x22002229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Madole & Harden argue that the Mendelian reshuffling of genes and genomes is analogous to randomised controlled trials. We are not convinced by their arguments. First, their recipe for meeting the demands on randomised experiments is inherently inconsistent. Second, disequilibrium across chromosomes conflicts with their assumption of statistical independence. Third, the genome-wide association study (GWAS) method has many pitfalls, including low repeatability.
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Affiliation(s)
- Mirjam J Borger
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands ; ; https://www.marmgroup.eu/
| | - Franz J Weissing
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands ; ; https://www.marmgroup.eu/
| | - Eva Boon
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands ; ; https://www.marmgroup.eu/
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Macdonald SJ, Cloud-Richardson KM, Sims-West DJ, Long AD. Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing. Genetics 2022; 220:iyab238. [PMID: 35100395 PMCID: PMC8893256 DOI: 10.1093/genetics/iyab238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/19/2021] [Indexed: 01/22/2024] Open
Abstract
Despite the value of recombinant inbred lines for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to recombinant inbred lines for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here, we describe such an extreme quantitative trait locus, or extreme quantitative trait loci, mapping strategy that builds on an existing multiparental population, the Drosophila Synthetic Population Resource, and involves phenotyping and genotyping a population derived by mixing hundreds of Drosophila Synthetic Population Resource recombinant inbred lines. Simulations demonstrate that challenging, yet experimentally tractable extreme quantitative trait loci designs (≥4 replicates, ≥5,000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional recombinant inbred line-based quantitative trait loci mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated extreme quantitative trait loci experiment that identifies 7 quantitative trait loci for caffeine resistance. Two mapped extreme quantitative trait loci factors replicate loci previously identified in recombinant inbred lines, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping extreme quantitative trait loci design has considerable advantages.
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Affiliation(s)
- Stuart J Macdonald
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS 66047, USA
| | | | - Dylan J Sims-West
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
| | - Anthony D Long
- Department of Ecology and Evolutionary Biology, University of California at Irvine, Irvine, CA 92697, USA
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Zou J, Gopalakrishnan S, Parker CC, Nicod J, Mott R, Cai N, Lionikas A, Davies RW, Palmer AA, Flint J. Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility. G3 (BETHESDA, MD.) 2022; 12:jkab394. [PMID: 34791208 PMCID: PMC8728023 DOI: 10.1093/g3journal/jkab394] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 10/17/2021] [Indexed: 12/12/2022]
Abstract
Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6, and GM11545 with bone mineral density, and Psmb9 with weight. However, replication at a nominal threshold of 0.05 between the two component studies was low, with less than one-third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner's Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Leveraging these observations, we integrated information about replication rates, study-specific heterogeneity, and Winner's Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.
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Affiliation(s)
- Jennifer Zou
- Department of Computer Science, University of California, Los Angeles, CA 90024, USA
| | - Shyam Gopalakrishnan
- Faculty of Health and Medical Sciences, GLOBE Institute, University of Copenhagen, Copenhagen DK-1353, Denmark
| | - Clarissa C Parker
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, VT 05753, USA
| | | | - Richard Mott
- UCL Department of Genetics, Evolution & Environment, UCL Genetics Institute, London WC1E 6BT, UK
| | - Na Cai
- Helmholtz Zentrum Muenchen, Helmoltz Pioneer Campus, Neuherberg 85764, Germany
| | - Arimantas Lionikas
- School of Medicine, Medical Sciences and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Robert W Davies
- Department of Statistics, University of Oxford, Oxford OX1 2JD, UK
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Jonathan Flint
- Department of Biobehavioral Sciences, University of California, Los Angeles, CA 90024, USA
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Montejo-Kovacevich G, Salazar PA, Smith SH, Gavilanes K, Bacquet CN, Chan YF, Jiggins CD, Meier JI, Nadeau NJ. Genomics of altitude-associated wing shape in two tropical butterflies. Mol Ecol 2021; 30:6387-6402. [PMID: 34233044 DOI: 10.1111/mec.16067] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 07/01/2021] [Indexed: 11/30/2022]
Abstract
Understanding how organisms adapt to their local environment is central to evolution. With new whole-genome sequencing technologies and the explosion of data, deciphering the genomic basis of complex traits that are ecologically relevant is becoming increasingly feasible. Here, we studied the genomic basis of wing shape in two Neotropical butterflies that inhabit large geographical ranges. Heliconius butterflies at high elevations have been shown to generally have rounder wings than those in the lowlands. We reared over 1,100 butterflies from 71 broods of H. erato and H. melpomene in common-garden conditions and showed that wing aspect ratio, that is, elongatedness, is highly heritable in both species and that elevation-associated wing aspect ratio differences are maintained. Genome-wide associations with a published data set of 666 whole genomes from across a hybrid zone, uncovered a highly polygenic basis to wing aspect ratio variation in the wild. We identified several genes that have roles in wing morphogenesis or wing aspect ratio variation in Drosophila flies, making them promising candidates for future studies. There was little evidence for molecular parallelism in the two species, with only one shared candidate gene, nor for a role of the four known colour pattern loci, except for optix in H. erato. Thus, we present the first insights into the heritability and genomic basis of within-species wing aspect ratio in two Heliconius species, adding to a growing body of evidence that polygenic adaptation may underlie many ecologically relevant traits.
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Affiliation(s)
| | | | - Sophie H Smith
- Animal and Plant Sciences, University of Sheffield, Sheffield, UK
| | | | | | | | - Chris D Jiggins
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Joana I Meier
- Department of Zoology, University of Cambridge, Cambridge, UK.,St John's College, University of Cambridge, Cambridge, UK
| | - Nicola J Nadeau
- Animal and Plant Sciences, University of Sheffield, Sheffield, UK
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